* copy some feasigns and corresponding embeddings from one sparse table to another
* copy all feasigns and corresponding embeddings from one sparse table to another
* copy all dense params from one table to another
* copy some local vars to other local vars

* support dump param to afs
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* code style
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* code style
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* dump param
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* dump param
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* dump param
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* dump param
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* add check nan / inf in downpour worker during training
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* support dump multi file
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* dump fix num file
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* add thread scope stat accurate metrics test=develop

* fix style

* fix style

* fix style

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix style test=develop

* fix conflict

* fix style

* fix style test=develop

* fix error test=develop

* fix error test=develop

* dump slot

* test

* proto

* dump slot

* test

* proto

* code style

* code style

* code style

* style

* add delete after unseen days

* add unseen days

* code style

* conflict solve
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* add clear model

* code style
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* code style
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* support debug tensor of each ins
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* support debug tensor of each ins
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* learning rate

* code style

* code style

* code style

* code style

* code style

* code style

* code style

* code style

* code style

* code style

* code style

* code style

* code style
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* code style
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* unitest

* style

* style

* multi phase

* add channel

* code style

* style

* style

* unitest

* style

* define

* define
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* style
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* rm define
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* linux

* linux
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* style
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* output format
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* windows ci
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adjust ins weight according to nid slot , user can specify adjust_ins_weight in strategy

Fix FleetWrapper:
1. fix shrink dense: just scale show
2. add datanorm scale: divide datanorm's gradient by batch_size

The change includes 2 things:

1. save delta model and shrink table are control by the same parameter before, now add delete_after_unseen_days to control shrink table.
2. value in sparse table has no slot before, now add slot in sparse table, and add DownpureCtrAccessor to support the new meta.
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Add Pipeline Concurrency Train Mode:
- Cpp: pipeline_trainer & section_worker
- Python: PipelineOptimizer
- Add a new data_feed type: PrivateInstantDataFeed
- Add a test demo of pipeline trainer and the test model is gnn
- Do not support win32 now

* add save/load model, shrink table, cvm, config file & fix pull dense bug
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* fix global shuffle bug, fix pull dense bug, fix release memeory bug, fix shrink error
add client flush, add get data size
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* fix global shuffle bug
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* fix global shuffle bug
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* fix code style
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* fix code style & modify pslib cmake
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* fix error of _role_maker
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* fix code style
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* fix code style
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* fix code style
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* fix code style
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* fix code style
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* fix windows compile error of fleet
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* fix global shuffle bug

* add comment
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* update pslib.cmake
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* fix fill sparse bug
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* fix push sparse bug
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add dist_multi_trainer for distributed training, add trainer_factory and device_worker_factory so that we can easily extend new training mode, add pull dense worker which is a singleton for parameter fetching